Development of a model to predict the risk of early graft failure after adult-to-adult living donor liver transplantation: An ELTR study

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dc.authoridPolak, Wojciech/0000-0002-3096-4893
dc.contributor.authorGiglio, Mariano Cesare
dc.contributor.authorDolce, Pasquale
dc.contributor.authorYilmaz, Sezai
dc.contributor.authorTokat, Yaman
dc.contributor.authorAcarli, Koray
dc.contributor.authorKilic, Murat
dc.contributor.authorZeytunlu, Murat
dc.date.accessioned2024-08-04T20:55:54Z
dc.date.available2024-08-04T20:55:54Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractGraft survival is a critical end point in adult-to-adult living donor liver transplantation (ALDLT), where graft procurement endangers the lives of healthy individuals. Therefore, ALDLT must be responsibly performed in the perspective of a positive harm-to-benefit ratio. This study aimed to develop a risk prediction model for early (3 months) graft failure (EGF) following ALDLT. Donor and recipient factors associated with EGF in ALDLT were studied using data from the European Liver Transplant Registry. An artificial neural network classification algorithm was trained on a set of 2073 ALDLTs, validated using cross-validation, tested on an independent random-split sample (n=518), and externally validated on United Network for Organ Sharing Standard Transplant Analysis and Research data. Model performance was assessed using the AUC, calibration plots, and decision curve analysis. Graft type, graft weight, level of hospitalization, and the severity of liver disease were associated with EGF. The model (http://ldlt.shinyapps.io/eltr_app) presented AUC values at cross-validation, in the independent test set, and at external validation of 0.69, 0.70, and 0.68, respectively. Model calibration was fair. The decision curve analysis indicated a positive net benefit of the model, with an estimated net reduction of 5-15 EGF per 100 ALDLTs. Estimated risks>40% and<5% had a specificity of 0.96 and sensitivity of 0.99 in predicting and excluding EGF, respectively. The model also stratified long-term graft survival (p<0.001), which ranged from 87% in the low-risk group to 60% in the high-risk group. In conclusion, based on a panel of donor and recipient variables, an artificial neural network can contribute to decision-making in ALDLT by predicting EGF risk.en_US
dc.description.sponsorshipAstellas; Novartis; Institut Georges Lopez; Sandoz; Chiesien_US
dc.description.sponsorshipThe ELTR is supported by grants from Astellas, Novartis, Institut Georges Lopez, Sandoz, and Chiesi and logistic support from the Paul Brousse Hospital(Assistance Publique-Hppitaux de Paris).en_US
dc.identifier.doi10.1097/LVT.0000000000000312
dc.identifier.endpage847en_US
dc.identifier.issn1527-6465
dc.identifier.issn1527-6473
dc.identifier.issue8en_US
dc.identifier.pmid38079264en_US
dc.identifier.scopus2-s2.0-85188832544en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage835en_US
dc.identifier.urihttps://doi.org/10.1097/LVT.0000000000000312
dc.identifier.urihttps://hdl.handle.net/11616/101904
dc.identifier.volume30en_US
dc.identifier.wosWOS:001140091200001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherLippincott Williams & Wilkinsen_US
dc.relation.ispartofLiver Transplantationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRecipienten_US
dc.subjectOutcomesen_US
dc.subjectImpacten_US
dc.subjectScoreen_US
dc.subjectSizeen_US
dc.subjectExperienceen_US
dc.subjectSteatosisen_US
dc.subjectDonationen_US
dc.subjectCohorten_US
dc.titleDevelopment of a model to predict the risk of early graft failure after adult-to-adult living donor liver transplantation: An ELTR studyen_US
dc.typeArticleen_US

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